Taught by people who still do the job.

Why practitioners, not professional trainers

The trade-off, stated honestly.

A full-time trainer teaching a subject they last practised in 2019 will be more polished, more available and easier to timetable. They will also teach you the version of the job that existed when they left it.

Our mentors are people whose weekday work is the subject they teach. The cost is scheduling difficulty and a smaller pool. The benefit is that when a platform changes, an algorithm updates or a tool is deprecated, the class hears it that week rather than in the next curriculum revision.

It also changes what students ask. A working mentor gets questions about real constraints — a client who will not approve the budget, a dataset with three years of inconsistent entries — and can answer from experience.

Analytics & BI

Business analysts and BI leads from IT services, banking and consumer businesses, who spend their weeks building the dashboards and models they teach.

Data science & ML

Practising data scientists and ML engineers who have put models into production and dealt with the drift and retraining that follows.

AI & LLM engineering

Engineers building RAG systems and agent pipelines in production, including at product companies and AI-first startups.

Cloud & DevOps

Platform, DevOps and SRE engineers who own infrastructure, cost and on-call in their day jobs.

Marketing & growth

Performance marketers and growth leads running live budgets for D2C brands, marketplaces and agencies.

Entrepreneurship & leadership

Founders, CXOs and senior operators who have raised rounds, hired teams and closed businesses — all three being instructive.

FinTech & trading

Practitioners from trading desks, FinTech products and risk functions who work with the instruments and systems taught.

Ninza Kids

Educators paired with practitioner guests, so children learn method from teachers and context from people doing the work.